




'''simple docstring'''







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
    from ...processing_utils import ProcessorMixin
    from ...utils import TensorType


lowerCAmelCase_     						=				logging.get_logger(__name__)

lowerCAmelCase_     						=				{
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}

class 	_snake_case(			UpperCAmelCase      ):
 __snake_case:							Tuple						=					'''layoutlmv3'''






 def __init__(self       :  Union[str, Any]    ,		a       :  Optional[int]=5_02_65    ,		a       :  Optional[int]=7_68    ,		a       :  List[Any]=12    ,		a       :  Tuple=12    ,		a       :  Optional[Any]=30_72    ,		a       :  List[Any]="gelu"    ,		a       :  str=0.1    ,		a       :  Any=0.1    ,		a       :  Optional[int]=5_12    ,		a       :  Dict=2    ,		a       :  List[Any]=0.02    ,		a       :  Optional[int]=1e-5    ,		a       :  Union[str, Any]=1    ,		a       :  Optional[int]=0    ,		a       :  Optional[int]=2    ,		a       :  Dict=10_24    ,		a       :  List[Any]=1_28    ,		a       :  Optional[Any]=1_28    ,		a       :  Optional[Any]=True    ,		a       :  List[str]=32    ,		a       :  Optional[Any]=1_28    ,		a       :  Optional[int]=64    ,		a       :  Any=2_56    ,		a       :  Optional[Any]=True    ,		a       :  Any=True    ,		a       :  List[str]=True    ,		a       :  Union[str, Any]=2_24    ,		a       :  Optional[int]=3    ,		a       :  Optional[int]=16    ,		a       :  Any=None    ,		**a       :  Dict    ,		)       ->   int:






    """simple docstring"""


    super().__init__(
        vocab_size=a    ,		hidden_size=a    ,		num_hidden_layers=a    ,		num_attention_heads=a    ,		intermediate_size=a    ,		hidden_act=a    ,		hidden_dropout_prob=a    ,		attention_probs_dropout_prob=a    ,		max_position_embeddings=a    ,		type_vocab_size=a    ,		initializer_range=a    ,		layer_norm_eps=a    ,		pad_token_id=a    ,		bos_token_id=a    ,		eos_token_id=a    ,		**a    ,		)
    A__       =     max_ad_position_embeddings
    A__       =     coordinate_size
    A__       =     shape_size
    A__       =     has_relative_attention_bias
    A__       =     rel_pos_bins
    A__       =     max_rel_pos
    A__       =     has_spatial_attention_bias
    A__       =     rel_ad_pos_bins
    A__       =     max_rel_ad_pos
    A__       =     text_embed
    A__       =     visual_embed
    A__       =     input_size
    A__       =     num_channels
    A__       =     patch_size
    A__       =     classifier_dropout




class 	_snake_case(			UpperCAmelCase      ):
 __snake_case:							Tuple						=					version.parse('''1.12'''      )






 @property
 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   Mapping[str, Mapping[int, str]]:






    """simple docstring"""


    if self.task in ["question-answering", "sequence-classification"]:
       return OrderedDict(
           [
               ('input_ids', {0: 'batch', 1: 'sequence'}),
               ('attention_mask', {0: 'batch', 1: 'sequence'}),
               ('bbox', {0: 'batch', 1: 'sequence'}),
               ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
           ]    )
    else:
       return OrderedDict(
           [
               ('input_ids', {0: 'batch', 1: 'sequence'}),
               ('bbox', {0: 'batch', 1: 'sequence'}),
               ('attention_mask', {0: 'batch', 1: 'sequence'}),
               ('pixel_values', {0: 'batch', 1: 'num_channels'}),
           ]    )






 @property
 def 						_UpperCamelCase (self       :  List[str]    )       ->   float:






    """simple docstring"""


    return 1e-5






 @property
 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   int:






    """simple docstring"""


    return 12






 def 						_UpperCamelCase (self       :  List[Any]    ,		a       :  "ProcessorMixin"    ,		a       :  int = -1    ,		a       :  int = -1    ,		a       :  bool = False    ,		a       :  Optional["TensorType"] = None    ,		a       :  int = 3    ,		a       :  int = 40    ,		a       :  int = 40    ,		)       ->   Mapping[str, Any]:






    """simple docstring"""


    setattr(processor.image_processor    ,		'apply_ocr'    ,		a    )

    # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
    A__       =     compute_effective_axis_dimension(
        a    ,		fixed_dimension=OnnxConfig.default_fixed_batch    ,		num_token_to_add=0    )
    # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
    A__       =     processor.tokenizer.num_special_tokens_to_add(a    )
    A__       =     compute_effective_axis_dimension(
        a    ,		fixed_dimension=OnnxConfig.default_fixed_sequence    ,		num_token_to_add=a    )
    # Generate dummy inputs according to compute batch and sequence
    A__       =     [[' '.join([processor.tokenizer.unk_token]    ) * seq_length]] * batch_size

    # Generate dummy bounding boxes
    A__       =     [[[48, 84, 73, 1_28]]] * batch_size

    # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
    # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
    A__       =     self._generate_dummy_images(a    ,		a    ,		a    ,		a    )

    A__       =     dict(
        processor(
            a    ,		text=a    ,		boxes=a    ,		return_tensors=a    ,		)    )

    return inputs








'''simple docstring'''







from torch import nn







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   if act_fn in ["swish", "silu"]:
      return nn.SiLU()
   elif act_fn == "mish":
      return nn.Mish()
   elif act_fn == "gelu":
      return nn.GELU()
   else:
      raise ValueError(F"""Unsupported activation function: {act_fn}"""						)


